# Using big data to develop universal and selective suicide prevention strategies

> **NIH VA IK2** · WHITE RIVER JUNCTION VA MEDICAL CENTER · 2024 · —

## Abstract

BACKGROUND: Suicide confers a massive burden to individuals and society, accounting for nearly 20
Veteran deaths every day. After controlling for age, Veterans’ suicide risk is 22% higher than US adult civilians.
In response, the US Department of Veterans Affairs (VA) has made suicide prevention its first priority. The
VA’s suicide prevention framework prioritizes patients at the highest risk for suicide, including those who
previously attempted suicide, were recently released from inpatient mental health treatment, or use opioids.
Although this strategy has led to improvements for high-risk patients, the majority of patients that die by suicide
are not included in this group. Indeed, less than 3% of recent VA suicide deaths were classified as high-risk by
the leading prediction metric. This proposal specifically focuses on this “non-high-risk” majority, i.e., those who
died by suicide, but whose risk was not detected by existing prediction mechanisms. This proposal leverages
big data to better identify, track, and treat this critical population. It has the potential to have a large impact on
Veteran health, broadening the reach of effective suicide prevention services.
OBJECTIVES: The long-term goal is for the candidate, Dr. Maxwell Levis, to become an independent clinical
researcher focused on developing, testing, and improving suicide prevention resources. His short-term
goals are to: 1) acquire skills in population-based approaches to suicide prediction and prevention, 2) improve
machine learning and natural language processing ability, and 3) gain experience adapting suicide prevention
resources. His research objectives align closely with these goals. Dr. Levis’ proposal’s central hypothesis is
that, through leveraging big data, he can better understand non-high-risk suicide decedents, and, in turn,
develop targeted suicide prediction and prevention mechanisms. In the award’s last two years, Dr. Levis will
submit a VA Merit Award proposal on leveraging psychotherapy to decrease suicide risk in this population.
METHODS: The VA’s suicide prevention framework relies on universal strategies to reach all patients (low-
risk), selective strategies to reach some patients (moderate-risk), and indicated strategies to reach the
relatively few patients with symptoms associated with suicide (high-risk). While strides have been made for
indicated strategies, comparable achievements have not been made for universal and selective strategies.
Using Corporate Data Warehouse data, Dr. Levis will develop a dataset of recent (2017–2018; n ≈ 4000) non-
high-risk suicide decedents (cases) and characterize this sample’s demographics, service and mental health
usage, and risk and treatment factors. He will then develop a suicide risk-matched (1:[10]) sample of VA
patients that did not die (controls), but shared similar risk, services, demographics, location, and treatment
intervals. Dr. Levis will then leverage cases’ and controls’ structured and unstructured elect...

## Key facts

- **NIH application ID:** 10803623
- **Project number:** 1IK2CX002630-01A1
- **Recipient organization:** WHITE RIVER JUNCTION VA MEDICAL CENTER
- **Principal Investigator:** Maxwell Eli Joshua Levis
- **Activity code:** IK2 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2024
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2024-07-01 → 2029-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10803623

## Citation

> US National Institutes of Health, RePORTER application 10803623, Using big data to develop universal and selective suicide prevention strategies (1IK2CX002630-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10803623. Licensed CC0.

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